Skip to content
Log in
Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

P. Senthilkumar

View on Semantic Scholar

4 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#federated learning Open access Oct 2026

QoS-Aware Secure Federated Intelligence for Biomedical Electronics and Medical Informatics in Wireless Health Monitoring Networks

Wireless Health Monitoring Networks (WHMNs) that utilize Biomedical Electronics (BMEs) are rapidly changing how medical information is gathered and used in contemporary Medical Informatics through providing ongoing, real-time, and remote assessment and monitoring of essential physiological parameters; including electro...

Anandalakshmi Thevampalayam Kaliappan, P. Senthilkumar, Tamilselvi K et al. · 0 citations
#federated learning Dataset Open access Sep 2026

Evaluating Federated continual learning for heart failure risk prediction: a simulation study

This study evaluates federated continual learning for heart failure risk prediction under changing clinical data conditions. Using a simulated 12-month, five-site dataset, it compares attention-based continual learning with FedAvg, FedProx, linear, and replay-based approaches. The findings show that apparent benefits o...

P. Senthilkumar · 0 citations
#federated learning Dataset Open access Sep 2026

Evaluating Federated continual learning for heart failure risk prediction: a simulation study

This study evaluates federated continual learning for heart failure risk prediction under changing clinical data conditions. Using a simulated 12-month, five-site dataset, it compares attention-based continual learning with FedAvg, FedProx, linear, and replay-based approaches. The findings show that apparent benefits o...

P. Senthilkumar · 0 citations
#reinforcement learning Dataset Open access Sep 2026

A Quantum-Inspired Dragonfly Optimization and Deep Reinforcement Learning Framework for Energy-Efficient Routing in Ad Hoc and IoT Networks

This repository presents QADO-DRL, a novel energy-efficient routing framework for wireless ad hoc and IoT networks that combines Quantum-Inspired Adaptive Dragonfly Optimization (QADO) with a Dueling Double Deep Q-Network (D3QN) routing agent. QADO uses quantum-superposition initialization and quantum rotation-gate upd...

P. Senthilkumar · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.